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https://github.com/finegrain-ai/refiners.git
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71 lines
3.5 KiB
Python
71 lines
3.5 KiB
Python
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import torch
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from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
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from torch import Tensor
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from refiners.fluxion.utils import manual_seed, no_grad
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import DoubleTextEncoder
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@no_grad()
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def test_double_text_encoder(
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diffusers_sdxl_pipeline: StableDiffusionXLPipeline,
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refiners_sdxl_text_encoder: DoubleTextEncoder,
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) -> None:
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"""Compare our refiners implementation with the diffusers implementation."""
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manual_seed(seed=0) # unnecessary, but just in case
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prompt = "A photo of a pizza."
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negative_prompt = ""
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atol = 1e-6
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( # encode text prompts using diffusers pipeline
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diffusers_embeds,
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diffusers_negative_embeds, # type: ignore
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diffusers_pooled_embeds, # type: ignore
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diffusers_negative_pooled_embeds, # type: ignore
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) = diffusers_sdxl_pipeline.encode_prompt(prompt=prompt, negative_prompt=negative_prompt)
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assert diffusers_negative_embeds is not None
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assert isinstance(diffusers_pooled_embeds, Tensor)
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assert isinstance(diffusers_negative_pooled_embeds, Tensor)
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# encode text prompts using refiners model
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refiners_embeds, refiners_pooled_embeds = refiners_sdxl_text_encoder(prompt)
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refiners_negative_embeds, refiners_negative_pooled_embeds = refiners_sdxl_text_encoder("")
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# check that the shapes are the same
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assert diffusers_embeds.shape == refiners_embeds.shape == (1, 77, 2048)
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assert diffusers_pooled_embeds.shape == refiners_pooled_embeds.shape == (1, 1280)
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assert diffusers_negative_embeds.shape == refiners_negative_embeds.shape == (1, 77, 2048)
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assert diffusers_negative_pooled_embeds.shape == refiners_negative_pooled_embeds.shape == (1, 1280)
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# check that the values are close
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assert torch.allclose(input=refiners_embeds, other=diffusers_embeds, atol=atol)
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assert torch.allclose(input=refiners_negative_embeds, other=diffusers_negative_embeds, atol=atol)
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assert torch.allclose(input=refiners_negative_pooled_embeds, other=diffusers_negative_pooled_embeds, atol=atol)
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assert torch.allclose(input=refiners_pooled_embeds, other=diffusers_pooled_embeds, atol=atol)
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@no_grad()
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def test_double_text_encoder_batched(refiners_sdxl_text_encoder: DoubleTextEncoder) -> None:
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"""Check that encoding two prompts works as expected whether batched or not."""
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manual_seed(seed=0) # unnecessary, but just in case
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prompt1 = "A photo of a pizza."
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prompt2 = "A giant duck."
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atol = 1e-6
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# encode the two prompts at once
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embeds_batched, pooled_embeds_batched = refiners_sdxl_text_encoder([prompt1, prompt2])
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assert embeds_batched.shape == (2, 77, 2048)
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assert pooled_embeds_batched.shape == (2, 1280)
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# encode the prompts one by one
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embeds_1, pooled_embeds_1 = refiners_sdxl_text_encoder(prompt1)
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embeds_2, pooled_embeds_2 = refiners_sdxl_text_encoder(prompt2)
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assert embeds_1.shape == embeds_2.shape == (1, 77, 2048)
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assert pooled_embeds_1.shape == pooled_embeds_2.shape == (1, 1280)
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# check that the values are close
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assert torch.allclose(input=embeds_1, other=embeds_batched[0].unsqueeze(0), atol=atol)
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assert torch.allclose(input=pooled_embeds_1, other=pooled_embeds_batched[0].unsqueeze(0), atol=atol)
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assert torch.allclose(input=embeds_2, other=embeds_batched[1].unsqueeze(0), atol=atol)
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assert torch.allclose(input=pooled_embeds_2, other=pooled_embeds_batched[1].unsqueeze(0), atol=atol)
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